How to use the dython._private.replace_nan_with_value function in dython

**Returns:** float in the range of [0,1]

    Parameters
    ----------
    x : list / NumPy ndarray / Pandas Series
        A sequence of categorical measurements
    y : list / NumPy ndarray / Pandas Series
        A sequence of categorical measurements
    nan_strategy : string, default = 'replace'
        How to handle missing values: can be either 'drop' to remove samples with missing values, or 'replace'
        to replace all missing values with the nan_replace_value. Missing values are None and np.nan.
    nan_replace_value : any, default = 0.0
        The value used to replace missing values with. Only applicable when nan_strategy is set to 'replace'.
    """
    if nan_strategy == REPLACE:
        x, y = replace_nan_with_value(x, y, nan_replace_value)
    elif nan_strategy == DROP:
        x, y = remove_incomplete_samples(x, y)
    s_xy = conditional_entropy(x,y)
    x_counter = Counter(x)
    total_occurrences = sum(x_counter.values())
    p_x = list(map(lambda n: n/total_occurrences, x_counter.values()))
    s_x = ss.entropy(p_x)
    if s_x == 0:
        return 1
    else:
        return (s_x - s_xy) / s_x

**Returns:** float

    Parameters
    ----------
    x : list / NumPy ndarray / Pandas Series
        A sequence of measurements
    y : list / NumPy ndarray / Pandas Series
        A sequence of measurements
    nan_strategy : string, default = 'replace'
        How to handle missing values: can be either 'drop' to remove samples with missing values, or 'replace'
        to replace all missing values with the nan_replace_value. Missing values are None and np.nan.
    nan_replace_value : any, default = 0.0
        The value used to replace missing values with. Only applicable when nan_strategy is set to 'replace'.
    """
    if nan_strategy == REPLACE:
        x, y = replace_nan_with_value(x, y, nan_replace_value)
    elif nan_strategy == DROP:
        x, y = remove_incomplete_samples(x, y)
    y_counter = Counter(y)
    xy_counter = Counter(list(zip(x,y)))
    total_occurrences = sum(y_counter.values())
    entropy = 0.0
    for xy in xy_counter.keys():
        p_xy = xy_counter[xy] / total_occurrences
        p_y = y_counter[xy[1]] / total_occurrences
        entropy += p_xy * math.log(p_y/p_xy)
    return entropy

**Returns:** float in the range of [0,1]

    Parameters
    ----------
    categories : list / NumPy ndarray / Pandas Series
        A sequence of categorical measurements
    measurements : list / NumPy ndarray / Pandas Series
        A sequence of continuous measurements
    nan_strategy : string, default = 'replace'
        How to handle missing values: can be either 'drop' to remove samples with missing values, or 'replace'
        to replace all missing values with the nan_replace_value. Missing values are None and np.nan.
    nan_replace_value : any, default = 0.0
        The value used to replace missing values with. Only applicable when nan_strategy is set to 'replace'.
    """
    if nan_strategy == REPLACE:
        categories, measurements = replace_nan_with_value(categories, measurements, nan_replace_value)
    elif nan_strategy == DROP:
        categories, measurements = remove_incomplete_samples(categories, measurements)
    categories = convert(categories, 'array')
    measurements = convert(measurements, 'array')
    fcat, _ = pd.factorize(categories)
    cat_num = np.max(fcat)+1
    y_avg_array = np.zeros(cat_num)
    n_array = np.zeros(cat_num)
    for i in range(0,cat_num):
        cat_measures = measurements[np.argwhere(fcat == i).flatten()]
        n_array[i] = len(cat_measures)
        y_avg_array[i] = np.average(cat_measures)
    y_total_avg = np.sum(np.multiply(y_avg_array,n_array))/np.sum(n_array)
    numerator = np.sum(np.multiply(n_array,np.power(np.subtract(y_avg_array,y_total_avg),2)))
    denominator = np.sum(np.power(np.subtract(measurements,y_total_avg),2))
    if numerator == 0:

**Returns:** float in the range of [0,1]

    Parameters
    ----------
    x : list / NumPy ndarray / Pandas Series
        A sequence of categorical measurements
    y : list / NumPy ndarray / Pandas Series
        A sequence of categorical measurements
    nan_strategy : string, default = 'replace'
        How to handle missing values: can be either 'drop' to remove samples with missing values, or 'replace'
        to replace all missing values with the nan_replace_value. Missing values are None and np.nan.
    nan_replace_value : any, default = 0.0
        The value used to replace missing values with. Only applicable when nan_strategy is set to 'replace'.
    """
    if nan_strategy == REPLACE:
        x, y = replace_nan_with_value(x, y, nan_replace_value)
    elif nan_strategy == DROP:
        x, y = remove_incomplete_samples(x, y)
    confusion_matrix = pd.crosstab(x,y)
    chi2 = ss.chi2_contingency(confusion_matrix)[0]
    n = confusion_matrix.sum().sum()
    phi2 = chi2/n
    r,k = confusion_matrix.shape
    phi2corr = max(0, phi2-((k-1)*(r-1))/(n-1))
    rcorr = r-((r-1)**2)/(n-1)
    kcorr = k-((k-1)**2)/(n-1)
    return np.sqrt(phi2corr/min((kcorr-1),(rcorr-1)))